Triple

T4226594
Position Surface form Disambiguated ID Type / Status
Subject Móstoles E94472 entity
Predicate hasTwinTown P919 FINISHED
Object Reus E388590 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Reus | Statement: [Móstoles, hasTwinTown, Reus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reus
Context triple: [Móstoles, hasTwinTown, Reus]
  • A. Reus chosen
    Reus is a city in Catalonia, Spain, known as the birthplace of architect Antoni Gaudí and for its historic center and vermouth production.
  • B. Lleida
    Lleida is a historic city in western Catalonia, Spain, known for its medieval Seu Vella cathedral and role as a regional agricultural and commercial center.
  • C. Esplugues de Llobregat
    Esplugues de Llobregat is a municipality in the metropolitan area of Barcelona, Catalonia, known for its residential character and proximity to the Catalan capital.
  • D. Sabadell
    Sabadell is a major industrial and commercial city in Catalonia, Spain, known historically for its textile industry and now as part of the Barcelona metropolitan area.
  • E. Mataró
    Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69b3453700a08190ae88792e3dc63207 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e4ed34c819081d1479ce87cd78c completed March 12, 2026, 11:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a85c79a881908cadc892dc30d8ef completed March 14, 2026, 6:26 p.m.
Created at: March 12, 2026, 11:04 p.m.